1. Technical Field
The present disclosure relates to segmentation and, more specifically, to a method and system for interactive segmentation using texture and intensity cues.
2. Discussion of Related Art
Image segmentation relates to the field of processing digital images to accurately distinguish between multiple objects that appear within the image. Image segmentation may be performed for either two-dimensional images or three-dimensional images, still images or moving images, and may be performed for all forms of images, regardless of their modality. While image segmentation may be performed on photographic images, image segmentation is particularly useful in the field of medical imaging. For example, image segmentation may be performed on CT or MR images to distinguish between various organs and other anatomical structures. By accurately distinguishing between anatomical structures within medical images, other medical image processing techniques may be more accurately performed, for example, to detect polyps, lesions, tumors from various portions of the body. Accordingly, image segmentation may play an important role in computer-aided detection of various diseases.
There are many techniques for performing image segmentation. These techniques may involve prompting a user to inspect acquired image data and to provide one or more seed locations that the user knows to be inside of the region to be segmented and/or outside of the region to be segmented. Using this information, image segmentation may be automatically performed by an image processing system to distinguish between image pixels/voxels that are inside the region to be segmented and pixels/voxels that are outside of the region to be segmented. Such segmentation techniques may be known as seed-based segmentation.
According to one simple approach for image segmentation, the user may provide a seed location that is understood to be inside the region to be segmented. A region growing algorithm may then be performed wherein each pixel/voxel adjacent to the selected seed is analyzed to determine whether it represents a change of intensity, with respect to the seed pixel/voxel, in excess of a predetermined threshold. So long as the adjacent pixel/voxel does not represent a sufficiently large change of intensity, that pixel/voxel may be considered to be part of the region to be segmented and that pixel/voxel may then be considered a seed for future iterations of this recursive process. The process may end when all pixels/voxels adjacent to the pixels/voxels that are considered to be part of the region to be segmented represent a change of intensity that is in excess of the predetermined threshold.
However, such techniques are not without problems. For example, where the boundaries of the region to be segmented include one or more points of weakness, for example, due to insufficient contrast between the region and its surrounding tissue, the growing algorithm may escape the boundary and generate an over-inclusive segmentation. Moreover, areas of excessive contrast within the region to be segmented may result in the growing algorithm stopping prematurely and thus generating an under-inclusive segmentation. Image noise may also adversely affect region growing techniques by obfuscating actual boundaries and creating the appearance of boundaries where none exist.
Texture may be a particular concern when dealing with seed-based segmentation because texture may include patterns of high-contrast pixels that may provide many opportunities for premature stopping and undue escaping and the “boundary” between tow textures may simply be a transition from more loosely ordered black dots on a white background to more tightly ordered black dots on a white background. In such a case, there may be no border of high-contrast to contain a segmentation algorithm.
FIGS. 1A-1C illustrate an exemplary seed-based segmentation problem. In FIG. 1A, the image 10 represents medical image data that is highly textured. As seen in FIG. 1B, the image 11 includes two seed locations designated by a user, the first seed location 11a represents a background location and the second seed location 11b represents a foreground location. The ultimate desired result may be seen in FIG. 1C where a segmented image 12 is provided that includes a white foreground image 13 and a black background image. However, as the difference between the foreground and the background is primarily a distinction between two different textures, growing algorithms such as those discussed above may be of little use.